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@ARTICLE{Kotzur:840174,
      author       = {Kotzur, Leander and Markewitz, Peter and Robinius, Martin
                      and Stolten, Detlef},
      title        = {{T}ime {S}eries {A}ggregation for {E}nergy {S}ystems
                      {D}esign: {M}odeling of {S}easonal {S}torage},
      journal      = {Applied energy},
      volume       = {213},
      issn         = {0306-2619},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2017-07729},
      pages        = {123 - 135},
      year         = {2017},
      abstract     = {The optimization-based design of renewable energy systems
                      is a computationally demanding task because of the high
                      temporal fluctuation of supply and demand time series. In
                      order to reduce these time series, the aggregation of
                      typical operation periods has become common. The problem
                      with this method is that these aggregated typical periods
                      are modeled independently and cannot exchange energy.
                      Therefore, seasonal storage cannot be adequately taken into
                      account, although this will be necessary for energy systems
                      with a high share of renewable generation.To address this
                      issue, this paper proposes a novel mathematical description
                      for storage inventories based on the superposition of
                      inter-period and intra-period states. Inter-period states
                      connect the typical periods and are able to account their
                      sequence. The approach has been adopted for different energy
                      system configurations. The results show that a significant
                      reduction in the computational load can be achieved also for
                      long term storage-based energy system models in comparison
                      to optimization models based on the full annual time
                      series.},
      cin          = {IEK-3},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-3-20101013},
      pnm          = {134 - Electrolysis and Hydrogen (POF3-134)},
      pid          = {G:(DE-HGF)POF3-134},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000425576900011},
      doi          = {10.1016/j.apenergy.2018.01.023},
      url          = {https://juser.fz-juelich.de/record/840174},
}